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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/tancik/fourier-feature-networks/blob/master/Experiments/1d_scatter_plots.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VviuYDC_wx_-",
        "colab_type": "code",
        "colab": {
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          "height": 51
        },
        "outputId": "217b7809-5592-42a0-8d31-42baa8e4aea3"
      },
      "source": [
        "!pip install -q neural_tangents==0.2.2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "  Building wheel for neural-tangents (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Building wheel for frozendict (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Wcn1yDT30vMF",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import jax\n",
        "from jax import random, grad, jit, vmap\n",
        "from jax.config import config\n",
        "from jax.lib import xla_bridge\n",
        "import jax.numpy as np\n",
        "import neural_tangents as nt\n",
        "from neural_tangents import stax\n",
        "from jax.experimental import optimizers\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import matplotlib.pylab as pylab\n",
        "from tqdm.notebook import tqdm as tqdm\n",
        "from matplotlib.lines import Line2D\n",
        "\n",
        "\n",
        "import time\n",
        "\n",
        "import numpy as onp\n",
        "\n",
        "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
        "colors = prop_cycle.by_key()['color']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gnC7achEw60P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Utils\n",
        "\n",
        "fplot = lambda x : np.fft.fftshift(np.log10(np.abs(np.fft.fft(x))))\n",
        "\n",
        "# Signal makers\n",
        "\n",
        "def sample_random_signal(key, decay_vec):\n",
        "  N = decay_vec.shape[0]\n",
        "  raw = random.normal(key, [N, 2]) @ np.array([1, 1j])\n",
        "  signal_f = raw * decay_vec\n",
        "  signal = np.real(np.fft.ifft(signal_f))\n",
        "  return signal\n",
        "\n",
        "def sample_random_powerlaw(key, N, power):\n",
        "  coords = np.float32(np.fft.ifftshift(1 + N//2 - np.abs(np.fft.fftshift(np.arange(N)) - N//2)))\n",
        "  decay_vec = coords ** -power\n",
        "  return sample_random_signal(key, decay_vec) # * 100\n",
        "\n",
        "\n",
        "# Network \n",
        "\n",
        "def make_network(num_layers, num_channels, num_outputs=1):\n",
        "  layers = []\n",
        "  for i in range(num_layers-1):\n",
        "      layers.append(stax.Dense(num_channels, parameterization='standard'))\n",
        "      layers.append(stax.Relu(do_backprop=True))\n",
        "  layers.append(stax.Dense(num_outputs, parameterization='standard'))\n",
        "  return stax.serial(*layers)\n",
        "\n",
        "# Encoding \n",
        "\n",
        "\n",
        "input_encoder = lambda x, a, b: np.concatenate([a * np.sin((2.*np.pi*x[...,None]) * b), \n",
        "                                                a * np.cos((2.*np.pi*x[...,None]) * b)], axis=-1) / np.linalg.norm(a) #* np.sqrt(a.shape[0])\n",
        "\n",
        "\n",
        "\n",
        "### complicated things\n",
        "\n",
        "\n",
        "def train_model_lite(rand_key, network_size, lr, iters, \n",
        "                train_input, test_input, optimizer, ab):\n",
        "    init_fn, apply_fn, kernel_fn = make_network(*network_size)\n",
        "    kernel_fn = jit(kernel_fn)\n",
        "\n",
        "    run_model = jit(lambda params, ab, x: np.squeeze(apply_fn(params, input_encoder(x, *ab))))\n",
        "    model_loss = jit(lambda params, ab, x, y: .5 * np.sum((run_model(params, ab, x) - y) ** 2))\n",
        "    model_psnr = jit(lambda params, ab, x, y: -10 * np.log10(np.mean((run_model(params, ab, x) - y) ** 2)))\n",
        "    model_grad_loss = jit(lambda params, ab, x, y: jax.grad(model_loss)(params, ab, x, y))\n",
        "\n",
        "    opt_init, opt_update, get_params = optimizer(lr)\n",
        "    opt_update = jit(opt_update)\n",
        "\n",
        "    _, params = init_fn(rand_key, (-1, input_encoder(train_input[0], *ab).shape[-1]))\n",
        "    opt_state = opt_init(params)\n",
        "\n",
        "    pred0 = run_model(get_params(opt_state), ab, test_input[0])\n",
        "    pred0_f = np.fft.fft(pred0)\n",
        "    \n",
        "    for i in (range(iters)):\n",
        "        opt_state = opt_update(i, model_grad_loss(get_params(opt_state), ab, *train_input), opt_state)\n",
        "    \n",
        "    train_psnr = model_psnr(get_params(opt_state), ab, *train_input)\n",
        "    test_psnr = model_psnr(get_params(opt_state), ab, *test_input)\n",
        "    # theory = predict_psnr(kernel_fn, np.fft.fft(test_input[1]), pred0_f, ab, i * lr)\n",
        "            \n",
        "    return get_params(opt_state), train_psnr, test_psnr #, theory"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "MXMEEOql0_Hn",
        "colab_type": "text"
      },
      "source": [
        "# Make fig 4"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tWMfr1H50_jd",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "N_train = 1024\n",
        "data_powers = [.5, 1.0, 1.5]\n",
        "# data_powers = [1.0]\n",
        "N_test_signals = 4\n",
        "\n",
        "N_embed = 16\n",
        "\n",
        "network_size = (4, 256)\n",
        "\n",
        "# learning_rate = 5e-3\n",
        "learning_rate = 2e-3\n",
        "sgd_iters = 1000"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TjOhNefO1C5z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "rand_key = random.PRNGKey(0)\n",
        "\n",
        "\n",
        "def data_maker(rand_key, N_pts, N_signals, p):\n",
        "  rand_key, *ensemble = random.split(rand_key, 1 + N_signals)\n",
        "  data = np.stack([sample_random_powerlaw(ensemble[i], N_pts, p) for i in range(N_signals)])\n",
        "  # data = (data - data.min(-1, keepdims=True)) / (data.max(-1, keepdims=True) - data.min(-1, keepdims=True))  - .5\n",
        "  data = (data - data.min()) / (data.max() - data.min())  - .5\n",
        "  return data, rand_key\n",
        "\n",
        "\n",
        "# Signal\n",
        "M = 2 ## Dont change\n",
        "N = N_train\n",
        "x_test = np.float32(np.linspace(0,1.,N*M,endpoint=False))\n",
        "x_train = x_test[::M]\n",
        "\n",
        "\n",
        "search_vals = 2. ** np.linspace(-5., 4., 1*8+1)\n",
        "\n",
        "bval_generators = {\n",
        "    'gaussian' : (32, lambda key, sc, N : random.normal(key, [N]) * sc),\n",
        "    'unif' : (64, lambda key, sc, N : random.uniform(key, [N]) * sc),\n",
        "    'power1' : (80, lambda key, sc, N : (sc ** random.uniform(key, [N]))),\n",
        "    'laplace' : (20, lambda key, sc, N : random.laplace(key, [N]) * sc),\n",
        "}\n"
      ],
      "execution_count": null,
      "outputs": []
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        },
        "outputId": "9d7cd852-2afd-4546-e295-f7a5d48379b2"
      },
      "source": [
        "\n",
        "names = list(bval_generators.keys())\n",
        "train_fn = lambda s, key, ab : train_model_lite(key, network_size, learning_rate, sgd_iters,\n",
        "                    (x_train, s[::2]), (x_test[1::2], s[1::2]), optimizers.adam, ab)\n",
        "\n",
        "\n",
        "best_powers = [.4, .75, 1.5]\n",
        "outputs_meta = []\n",
        "dense_meta = []\n",
        "s_lists = []\n",
        "for p, bp in tqdm(zip(data_powers, best_powers)):\n",
        "  s_list, rand_key = data_maker(rand_key, N*M, N_test_signals, p)\n",
        "  s_lists.append(s_list)\n",
        "\n",
        "  b = np.float32(np.arange(1, N//2+1))\n",
        "  ab_dense = (b ** -bp, b)\n",
        "  rand_key, *ensemble_key = random.split(rand_key, 1+s_list.shape[0])\n",
        "  ensemble_key = np.array(ensemble_key)\n",
        "  ab_samples = np.array([ab_dense] * s_list.shape[0])\n",
        "  outputs_dense = vmap(train_fn, in_axes=(0, 0, 0))(s_list, ensemble_key, ab_samples)\n",
        "  dense_meta.append(outputs_dense)\n",
        "\n",
        "  outputs = []\n",
        "  \n",
        "  for sc in tqdm(search_vals, leave=False):\n",
        "    outputs.append([])\n",
        "\n",
        "    for k in tqdm(names, leave=False):\n",
        "      \n",
        "      rand_key, *ensemble_key = random.split(rand_key, 1+s_list.shape[0])\n",
        "      ensemble_key = np.array(ensemble_key)\n",
        "\n",
        "      factor, b_fn = bval_generators[k]\n",
        "      ab_samples = np.array([(np.ones([N_embed]), b_fn(ensemble_key[i], factor * sc, N_embed)) for i in range(s_list.shape[0])])\n",
        "      rand_key, *ensemble_key = random.split(rand_key, 1+s_list.shape[0])\n",
        "      ensemble_key = np.array(ensemble_key)\n",
        "      \n",
        "      z = vmap(train_fn, in_axes=(0, 0, 0))(s_list, ensemble_key, ab_samples)\n",
        "      outputs[-1].append(list(z) + [ab_samples])\n",
        "      \n",
        "\n",
        "  outputs_meta.append(outputs)\n"
      ],
      "execution_count": null,
      "outputs": [
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            "text/plain": [
              "HBox(children=(FloatProgress(value=0.0, max=4.0), HTML(value='')))"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Q73j1ktUpHXx",
        "colab_type": "text"
      },
      "source": [
        "# Make Figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WYhgkC9v8hqM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 398
        },
        "outputId": "12c653f0-f058-4a89-c14d-a6c5c9449b8f"
      },
      "source": [
        "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
        "colors = prop_cycle.by_key()['color']\n",
        "\n",
        "params = {'legend.fontsize': 22,\n",
        "         'axes.labelsize': 22,\n",
        "         'axes.titlesize': 26,\n",
        "         'xtick.labelsize':18,\n",
        "         'ytick.labelsize':18}\n",
        "pylab.rcParams.update(params)\n",
        "\n",
        "\n",
        "matplotlib.rcParams['mathtext.fontset'] = 'cm'\n",
        "matplotlib.rcParams['mathtext.rm'] = 'serif'\n",
        "\n",
        "plt.rcParams[\"font.family\"] = \"cmr10\"\n",
        "\n",
        "colors_k = np.array([[0.8872, 0.4281, 0.1875],\n",
        "    [0.8136, 0.6844, 0.0696],\n",
        "    [0.2634, 0.6634, 0.4134],\n",
        "    [0.0943, 0.5937, 0.8793],\n",
        "    [0.3936, 0.2946, 0.6330],\n",
        "    [0.7123, 0.2705, 0.3795]])\n",
        "\n",
        "legend_map = {\n",
        "    'gaussian' : \"Gaussian\",\n",
        "    'unif' : \"Uniform\",\n",
        "    'power1' : r\"Uniform $\\log$\",\n",
        "    'laplace' : \"Laplacian\",\n",
        "}\n",
        "markers = {\n",
        "    'gaussian' : \"x\",\n",
        "    'unif' : \"D\",\n",
        "    'power1' : \"s\",\n",
        "    'laplace' : \"v\",\n",
        "}\n",
        "\n",
        "plt.figure(figsize=(20,4))\n",
        "plot_ind = 0\n",
        "letters = ['a','b','c']\n",
        "for data_power, outputs, outputs_dense in zip(data_powers, outputs_meta, dense_meta):\n",
        "  plot_ind += 1\n",
        "  if plot_ind == 4:\n",
        "      continue\n",
        "  plt.subplot(1,3,plot_ind)\n",
        "  ax = plt.gca()\n",
        "  errs_dense = outputs_dense[2]\n",
        "  errs_dense = 10**(errs_dense/-10)\n",
        "  errs_dense_std = np.std(errs_dense)\n",
        "  errs_dense = np.mean(errs_dense)\n",
        "\n",
        "  marker_list = []\n",
        "  for i, k in enumerate(names):\n",
        "    data = [r[i] for r in outputs]\n",
        "\n",
        "    bvals = np.array([z[-1] for z in data])[:,:,1]\n",
        "\n",
        "    b_stds = np.sqrt(np.mean(bvals**2, -1))\n",
        "\n",
        "    offset_vec = 0.\n",
        "\n",
        "    errs = np.array([z[2] for z in data]) - offset_vec\n",
        "    errs = 10**(errs.ravel()/-10)\n",
        "    \n",
        "    plt.scatter(b_stds.ravel(), errs, label=legend_map[k], marker='o', alpha=.75, s=25, c=[colors_k[i]])\n",
        "    plt.grid(True, which='major', alpha=.3)\n",
        "    plt.yscale('log')\n",
        "\n",
        "  if plot_ind == 1:\n",
        "      under_loc = (0.16, 1.05)\n",
        "      over_loc = (0.88, 1.05)\n",
        "      under_end = .32\n",
        "      over_start = .76\n",
        "  elif plot_ind == 2:\n",
        "      under_loc = (0.22, 1.05)\n",
        "      over_loc = (0.84, 1.05)\n",
        "      under_end = .44\n",
        "      over_start = .69\n",
        "  else:\n",
        "      under_loc = (0.17, 1.05)\n",
        "      over_loc = (0.745, 1.05)\n",
        "      under_end = .34\n",
        "      over_start = .5\n",
        "  props = dict(boxstyle='round,pad=0.35,rounding_size=.1', facecolor='white', edgecolor='gray', linewidth=.3, alpha=0.95)\n",
        "  plt.text(*under_loc, 'Underfitting', horizontalalignment='center', \n",
        "    verticalalignment='center', transform=ax.transAxes, fontsize=18)\n",
        "  plt.text(*over_loc, 'Overfitting', horizontalalignment='center', \n",
        "    verticalalignment='center', transform=ax.transAxes, fontsize=18)\n",
        "  \n",
        "  kwargs = dict(transform=ax.transAxes, linewidth=4, color='k', clip_on=False)\n",
        "  ax.plot((0.005, under_end), (.99, .99), **kwargs)\n",
        "  ax.plot((over_start, .995), (.99, .99), **kwargs)\n",
        "\n",
        "\n",
        "\n",
        "  plt.hlines(errs_dense, 0, 2**11, alpha=.5, linestyles='--', label='Dense')\n",
        "  plt.fill_between([0,2**11], errs_dense-errs_dense_std, errs_dense+errs_dense_std, color='k', alpha=.05)\n",
        "\n",
        "  ax.set_xscale('log', basex=2)\n",
        "  plt.xlabel('Standard deviation of sampled $b_i$')\n",
        "  plt.xlim((0,2**10))\n",
        "  if plot_ind == 1:\n",
        "    plt.ylabel('Mean squared error' )\n",
        "  plt.title(fr'({letters[plot_ind-1]}) Data sampled from $1/f^' + r'{' + fr'{data_power}' + r'}$', y=-.43)\n",
        "  ax.set_yticklabels([], minor=True)\n",
        "  if plot_ind==3:\n",
        "    plt.legend(loc='center left', bbox_to_anchor=(1,.5), handlelength=1)\n",
        "\n",
        "plt.savefig('fig_1d_sweeps.pdf', bbox_inches='tight', pad_inches=0)\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:100: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
            "Invalid limit will be ignored.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zozjbndxpKPq",
        "colab_type": "text"
      },
      "source": [
        "# Make supplement figure"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8SGr1CUgHh_z",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 379
        },
        "outputId": "245d0106-088d-467c-c215-c37fa928b352"
      },
      "source": [
        "prop_cycle = plt.rcParams['axes.prop_cycle']\n",
        "colors = prop_cycle.by_key()['color']\n",
        "\n",
        "params = {'legend.fontsize': 22,\n",
        "         'axes.labelsize': 22,\n",
        "         'axes.titlesize': 26,\n",
        "         'xtick.labelsize':18,\n",
        "         'ytick.labelsize':18}\n",
        "pylab.rcParams.update(params)\n",
        "\n",
        "\n",
        "matplotlib.rcParams['mathtext.fontset'] = 'cm'\n",
        "matplotlib.rcParams['mathtext.rm'] = 'serif'\n",
        "\n",
        "plt.rcParams[\"font.family\"] = \"cmr10\"\n",
        "\n",
        "colors_k = np.array([[0.8872, 0.4281, 0.1875],\n",
        "    [0.8136, 0.6844, 0.0696],\n",
        "    [0.2634, 0.6634, 0.4134],\n",
        "    [0.0943, 0.5937, 0.8793],\n",
        "    [0.3936, 0.2946, 0.6330],\n",
        "    [0.7123, 0.2705, 0.3795]])\n",
        "\n",
        "legend_map = {\n",
        "    'gaussian' : \"Gaussian\",\n",
        "    'unif' : \"Uniform\",\n",
        "    'power1' : r\"$1/f$\",\n",
        "    'laplace' : \"Laplacian\",\n",
        "}\n",
        "markers = {\n",
        "    'gaussian' : \"x\",\n",
        "    'unif' : \"D\",\n",
        "    'power1' : \"s\",\n",
        "    'laplace' : \"v\",\n",
        "}\n",
        "\n",
        "plt.figure(figsize=(20,4))\n",
        "plot_ind = 0\n",
        "letters = ['a','b','c']\n",
        "for data_power, outputs, outputs_dense in zip(data_powers, outputs_meta, dense_meta):\n",
        "  plot_ind += 1\n",
        "  if plot_ind == 4:\n",
        "      continue\n",
        "  plt.subplot(1,3,plot_ind)\n",
        "  ax = plt.gca()\n",
        "\n",
        "  marker_list = []\n",
        "  for i, k in enumerate(names):\n",
        "    data = [r[i] for r in outputs]\n",
        "\n",
        "    bvals = np.array([z[-1] for z in data])[:,:,1]\n",
        "\n",
        "    b_stds = np.sqrt(np.mean(bvals**2, -1)) #.mean(-1)\n",
        "\n",
        "    offset_vec = 0.\n",
        "\n",
        "    errs_train = np.array([z[1] for z in data]) - offset_vec\n",
        "    errs_train = 10**(errs_train.ravel()/-10)\n",
        "\n",
        "    errs = np.array([z[2] for z in data]) - offset_vec\n",
        "    errs = 10**(errs.ravel()/-10)\n",
        "\n",
        "    plt.scatter(b_stds.ravel(), errs, label=legend_map[k], marker=markers[k], alpha=.75, s=25, c=[colors_k[0]])\n",
        "    if i == 0:\n",
        "        plt.scatter(b_stds.ravel(), errs_train, label='Training', marker=markers[k], alpha=.75, s=25, c=[colors_k[3]])\n",
        "    else:\n",
        "        plt.scatter(b_stds.ravel(), errs_train, marker=markers[k], alpha=.75, s=25, c=[colors_k[3]])\n",
        "    plt.grid(True, which='major', alpha=.3)\n",
        "    plt.yscale('log')\n",
        "\n",
        "  ax.set_xscale('log', basex=2)\n",
        "  plt.xlabel('Standard deviation of sampled $b_i$')\n",
        "  plt.xlim((0,2**10))\n",
        "  if plot_ind == 1:\n",
        "    plt.ylabel('Mean squared error' )\n",
        "  plt.title(fr'({letters[plot_ind-1]}) Data sampled from $\\alpha={data_power}$', y=-.43)\n",
        "  ax.set_yticklabels([], minor=True)\n",
        "  if plot_ind==3:\n",
        "    custom_lines = [Line2D([], [], marker=markers[names[0]], color='gray', markersize=10, linestyle='None'),\n",
        "                    Line2D([], [], marker=markers[names[1]], color='gray', markersize=10, linestyle='None'),\n",
        "                    Line2D([], [], marker=markers[names[2]], color='gray', markersize=10, linestyle='None'),\n",
        "                    Line2D([], [], marker=markers[names[3]], color='gray', markersize=10, linestyle='None'),\n",
        "                    Line2D([], [], marker='o', color=colors_k[0], markersize=15, linestyle='None'),\n",
        "                    Line2D([], [], marker='o', color=colors_k[3], markersize=15, linestyle='None')]\n",
        "\n",
        "    ax.legend(custom_lines, ['Gaussian', 'Uniform', 'Uniform log', 'Laplacian', 'Test', 'Train'], loc='center left', bbox_to_anchor=(1,.5),  ncol=1, framealpha=.95, handlelength=1.6)\n",
        "    # plt.legend(loc='center left', bbox_to_anchor=(1,.5), handlelength=1)\n",
        "\n",
        "plt.savefig('fig_1d_sweeps_supp.pdf', bbox_inches='tight', pad_inches=0)\n",
        "\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:73: UserWarning: Attempted to set non-positive left xlim on a log-scaled axis.\n",
            "Invalid limit will be ignored.\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x288 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DXlGMooGVNq6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}
